CN106248368B - Combustion engine turbine blade fault detection method based on deep learning - Google Patents
Combustion engine turbine blade fault detection method based on deep learning Download PDFInfo
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Abstract
The invention belongs to the field of fault detection of gas turbines, and particularly relates to a deep learning-based fault detection method for turbine blades of a gas turbine. The invention comprises the following steps: (1) preprocessing temperature data of the turbine blade; (2) extracting a turbine blade temperature characteristic vector; (3) fault diagnosis based on a deep learning network; (4) turbine blade fault detection for a combustion engine. Aiming at the problem that the temperature acquisition data sample of the turbine blade is large, the deep learning method is introduced into the fault diagnosis of the turbine blade of the gas turbine for the first time, so that the diversity development of the fault diagnosis of the turbine blade of the gas turbine is promoted, and the accuracy of fault detection is improved.
Description
Technical Field
The invention belongs to the field of fault detection of gas turbines, and particularly relates to a deep learning-based fault detection method for turbine blades of a gas turbine.
Background
Gas turbine engines, also known as gas turbines, which are a form of heat engine, have been used in the last 30 centuries for large military and civilian applications such as aviation and ships, and have been regarded as an important manifestation of the national comprehensive strength since the invention. Since 1960, the gas turbine comprehensively replaces a piston engine, the gas turbine is applied suddenly and violently, and the gas turbine also becomes irreplaceable in a generator and a propulsion system of a tank in each country. Because the device has the excellent characteristics of small volume, light weight, less pollution, high reliability, outstanding cost performance, extremely high efficiency and the like, the device reflects the utilization value of the device again under the large-premise environment of improving the environment and adjusting the energy industry mechanism since the 21 st century, so far, the industrial scale of the device becomes larger and larger, and the product application becomes wider and wider.
The main structure of the gas turbine mainly comprises a combustion chamber, a gas compressor, a turbine, a tail nozzle and other components, and can be divided into the following parts according to classification: the turbo jet engine, the turbo fan engine, the turboprop engine, the turbine bearing engine and the like, wherein the turbo fan engine is most widely applied, and the aviation field, the ship field and the generator system mostly adopt the turbo fan engine as a preferred scheme for providing power. The gas turbine industry in China starts late, but develops rapidly, and through years of scientific research and development, China makes great progress in the aspects of independent innovation of the gas turbine, product development, digestion and absorption and foreign advanced technology. Meanwhile, due to the adjustment of the structure of the energy industry, the yield of the natural gas is continuously increased, and good conditions and environment are provided for the development of the gas turbine taking the natural gas as the fuel. According to the plan of national development and improvement committee, the total installed quantity of the gas turbine in China reaches 5500 ten thousand kilowatts as far as 2020, and meanwhile, the gas turbine becomes the most huge gas turbine development market in the world and has infinite prospect.
Deep learning is an artificial intelligence mode proposed by Hinton et al in 2006, which can abstract low-level features into high-level features closer to the nature of the features, has been successfully implemented in handwriting recognition, speech recognition, face recognition and the like, and is a hot point of current research. The principle of the traditional machine learning method such as hidden Markov model, support vector machine, maximum entropy model and the like in processing feature vectors is to map the original input features into feature intervals, so that the feature structure becomes simpler and has certain limitation. However, these methods tend to be frustrating when faced with the classification of complex problems when sample capacity and computational power are limited. In contrast, the deep learning mode can deal with the problem of extracting essential features of a data set from a large-capacity sample, effectively approximates a complex function, and classifies input data after being processed.
Disclosure of Invention
The invention aims to provide a turbine blade fault detection method of a combustion engine based on deep learning, which can improve the fault detection fault rate.
The purpose of the invention is realized as follows:
(1) the temperature data preprocessing of the turbine blade comprises the following steps:
(1.1) periodic Signal extraction
The turbine engine analyzed consisted of 86 blades, the data for radiation thermometry was obtained after a number of cycles of rotation of all the blades,
according to the discriminant formula of the periodic property: f (x) ═ f (x + T)
Wherein T is the period of the function, and whether the temperature data is period data is judged by the above formula; the minimum value and the maximum value are taken as the discrimination standard to carry out the period discrimination,
taking a certain maximum value point as a starting point, counting all the maximum value points in a certain section of interval, calling the points as test points, obtaining two adjacent test points with the same temperature, then respectively sampling backwards and comparing the temperatures of the sampling points, and if the two adjacent test points with the same temperature are the same, determining that the period is one;
(1.2) variable Condition temperature data distribution
The working conditions are divided into the following three working conditions: 0.6 operating mode, 0.8 operating mode and 1.0 operating mode, the turbine blade rotational speed under three kinds of operating modes respectively is: 8635/rpm, 8920/rpm, 9138/rpm;
(1.3) Single blade temperature data distribution
Establishing a segmentation algorithm for single blade temperature data on extreme value searching, counting maximum value points in the data, comparing the temperature data of each extreme value point, and selecting the point with the maximum temperature data, namely the maximum value point, and marking as a0With a0Searching a period backwards as a starting point, namely 40 sampling points, and recording a maximum value point in the period; making a judgment on the maximum value points, when the maximum value points cannot be larger than 10% of the adjacent maximum value points, if the maximum value points are larger than 10% of the adjacent maximum value points, changing the maximum value points into the starting points a0The process is circulated for 86 times to obtain the period of 86 blades, the sequence numbers from 01 to 86 are carried out on the blades, and the extracted features are used for state classification detection;
(2) extracting turbine blade temperature feature vectors
Extracting the temperature characteristic vector of the blade by adopting a method based on ensemble empirical mode decomposition; changing the number of extreme points and the distribution interval of the extreme points of the signal or data by adding different Gaussian noises with given amplitudes into the signal every time, and then carrying out overall average cancellation on IMF components obtained by multiple decomposition to cancel the noises added into the signal;
(2.1) initializing a noise amplitude value added to the signal;
(2.2) decomposing the ith added noise signal;
(2.2.1) adding a Gaussian signal n with a certain amplitude to the signal x (t)i(t)，xi(t)＝ni(t) + x (t), where the left side of the equation represents the signal formed after the i-th addition of the noise signal; the first term on the right of the equation represents the gaussian noise signal; x (t) represents the original signal;
(2.2.2) for xi(t) EMD decomposition to obtain a set of IMF components ci(I ═ 1,2, 3.., I), where c isiRepresenting IMF components obtained by the ith decomposition; i represents the number of decompositions;
(2.2.3) if I < I, returning to step (2.2.1) and so that I ═ I +1, repeating step (2.2.1) and step (2.2.2) until I ═ I;
(2.3) calculating the overall average value by the I decomposition, denoted by the symbol y;
(2.4) saving the total average value of all IMF components as a final IMF component;
(3) fault diagnosis based on deep learning network
Selecting a restricted Boltzmann machine model as a depth network design model, adjusting the restricted Boltzmann machine model through the progressive between layers, forming a transmission layer of the whole depth model by using 1 hidden layer and 1 visual output layer, and initializing the weight of the whole network;
the restricted Boltzmann model is expressed as RBM (W, b, c, v)0) Where W is the weight connection matrix between the first hidden layer and the second hidden layer, b is the bias of the hidden layer, c is the bias of the input layer, v0Represents a sample set used in training;
(3.1) for all nodes i of the hidden layer, calculating mapping operation between the hidden layersI.e. P (h)0i＝1|v0) According to P (h)0i＝1|v0) Sampling to obtain h0iSigm () is a mapping function;
(3.2) for all nodes j of the visual layers, calculating the mapping operation between the visual layersI.e. P (v)1j＝1|h0) According to P (v)1j＝1|h0) Sampling to obtain v1j；
(3.3) for the used hidden layer node i, calculating the mapping operation between the hidden layer and the hidden layerI.e. P (h)1i＝1|v1) According to P (h)1i＝1|v1) Updating the bias parameters of the connection weight; the update formula is as follows:
W＝W-ε(h0v0'-Q(h1＝1|v1)v1')
b＝b-ε(h0-Q(h1＝1|v1))
c＝c-ε(v0-v1)
(4) detecting faults of turbine blades of a combustion engine: constructing a gas turbine blade fault detection system for an algorithm experiment, simulating 6 types of fault data, 10 groups of samples of each type, and 60 groups of data, carrying out EEMD decomposition on a simulated turbine blade fault signal, then solving the energy of IMF of the simulated turbine blade fault signal, selecting the first 8 components, enabling the capacity of a residual item to be zero, and then solving a feature vector; dividing the data into two parts, randomly taking 48 groups as training samples, and taking the other 12 groups as test samples; training the deep learning network by using the training samples; and classifying the samples to be tested by using the trained deep learning network, and outputting a classification result.
The invention has the beneficial effects that: aiming at the problem that the temperature acquisition data sample of the turbine blade is large, the deep learning method is introduced into the fault diagnosis of the turbine blade of the gas turbine for the first time, so that the diversity development of the fault diagnosis of the turbine blade of the gas turbine is promoted, and the accuracy of fault detection is improved.
Drawings
FIG. 1 is a block diagram of turbine blade fault detection based on deep learning;
FIG. 2 turbine blade segmentation flow chart.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The invention provides a turbine blade fault detection method of a combustion engine based on deep learning, which comprises the steps of preprocessing turbine blade temperature data, including periodic signal extraction and segmentation, different working condition temperature data analysis, single blade temperature data distribution research, then extracting the characteristics of a turbine blade, and extracting the characteristic vector of the turbine blade by adopting a method based on Ensemble Empirical Mode Decomposition (EEMD). And finally, carrying out turbine blade fault diagnosis based on the deep learning network. Compared with a BP neural network and a support vector machine, the fault diagnosis accuracy is higher.
The invention relates to a method for detecting faults of turbine blades of a gas turbine, which comprises the steps of analyzing and preprocessing temperature data of the turbine blades, extracting periodic signals and analyzing data under different working conditions, segmenting the temperature data of single turbine blades, extracting temperature characteristic vectors of the turbine blades, and then detecting the faults of the turbine blades of the gas turbine based on a deep learning theory.
The invention relates to a method for detecting faults of turbine blades of a gas turbine, which comprises the steps of preprocessing temperature data of the turbine blades to obtain data of a single turbine blade in one period under different working conditions, segmenting the temperature data of the single turbine blade, extracting a temperature characteristic vector of the turbine blade, and then detecting the faults of the turbine blades of the gas turbine based on a BP artificial network adopting a depth model.
The method comprises the following concrete implementation steps:
(1) the temperature data preprocessing of the turbine blade comprises the following steps:
extraction of periodic signal
The turbine engine analyzed consisted of 86 blades and the data for radiation thermometry was obtained after a number of cycles of rotation of all the blades.
According to the discriminant formula of the periodic property: f (x) ═ f (x + T)
Where T is the period of the function, whether these temperature data are period data is determined by the above equation. And (3) taking the minimum value and the maximum value as discrimination standards to carry out period judgment, and finding out that almost the same waveform appears at intervals of a sampling interval, so that the acquired temperature data is judged to be period data.
Taking a certain maximum value point as a starting point, counting all the maximum value points in a certain section of interval, calling the points as 'test points', obtaining two adjacent test points with the same temperature, then respectively sampling backwards and comparing the temperatures of the sampling points, and if the two adjacent test points with the same temperature are the same, determining that the period is the period.
Operating mode-changing temperature data distribution
The rotating speed of the turbine is fast when the turbine runs, so that the specific moment when the fault occurs cannot be determined, and the rotating speed condition of the turbine engine at a certain temperature in the working process within a certain period of time when the fault occurs can only be determined. The working conditions are divided into the following three working conditions: 0.6 operating mode, 0.8 operating mode and 1.0 operating mode, the turbine blade rotational speed under three kinds of operating modes respectively is: 8635/rpm, 8920/rpm, 9138/rpm.
Distribution of single blade temperature data
In turbine engines, there is an overlap between blades, and therefore, the temperature data needs to be further analyzed to identify specific blade positions. Establishing a segmentation algorithm for single blade temperature data on extreme value searching, counting extreme values in the data by taking a maximum value as an example, comparing the temperature data of all the extreme values, and selecting a point with the maximum temperature data, namely a maximum value point, recorded as a0With a0For the starting point, a cycle, i.e. 40 sample points, is searched backwards, and the maximum point within the cycle is recorded. Making a decision on the maxima points when the maxima points cannot be greater than 10% of their neighbors, e.g.If it is more than 10% of the neighboring points, the maximum point is changed to the starting point a0The method is circulated for 86 times, the period of 86 blades is obtained, the 01 to 86 sequence numbers are carried out on the blades, and the characteristics of the blades can be extracted for state classification detection.
(2) Turbine blade temperature feature vector extraction
The extraction of the blade temperature feature vector is performed by a method based on Ensemble Empirical Mode Decomposition (EEMD). EEMD is a signal analysis method which assists analysis through noise, the method utilizes the characteristic that white Gaussian noise has uniform frequency distribution to solve the modal aliasing phenomenon caused by Decomposition of an Empirical Mode Decomposition (EMD) method, changes the number of extreme points and the distribution interval of the extreme points of a signal or data by adding different Gaussian noise with given amplitude into the signal every time, and then performs overall averaging on IMF components obtained by multiple Decomposition to achieve the purpose of offsetting the noise added into the signal, thereby effectively avoiding the appearance of modal aliasing.
The noise amplitude values added to the signal are initialized.
Secondly, decomposing the noise signal added for the ith time:
a. adding a Gaussian signal n with a certain amplitude to the signal x (t)i(t)，xi(t)＝ni(t) + x (t), where the left side of the equation represents the signal formed after the i-th addition of the noise signal; the first term on the right of the equation represents the gaussian noise signal; x (t) represents the original signal.
b. For xi(t) EMD decomposition to obtain a set of IMF components ci(I ═ 1,2, 3.., I), where c isiRepresenting IMF components obtained by the ith decomposition; i represents the number of decompositions.
c. If I < I, return to step a and let I +1, repeat steps a and b until I.
And calculating the overall average value of the I decomposition times and indicating the overall average value by the symbol y.
And fourthly, saving the total average value of all IMF components as the final IMF component.
(3) Fault diagnosis based on deep learning network
And selecting a Restricted Boltzmann Machine (RBM) as a depth network design model. The most important of these is the regulation of RBM. RBM regulation is regulated by progressive layers, a transmission layer of the whole depth model is formed by using 1 hidden layer and 1 visual output layer, and the weight of the whole network is initialized.
The RBM model is represented as RBM (W, b, c, v)0) Where W is the weight connection matrix between the first hidden layer and the second hidden layer, b is the bias of the hidden layer, c is the bias of the input layer, v0Representing the sample set used in training.
Calculating mapping operation between hidden layers for nodes i of all hidden layersI.e. P (h)0i＝1|v0) According to P (h)0i＝1|v0) Sampling to obtain h0iSigm () is a mapping function.
Calculating mapping operation between visual layers for nodes j of all visual layersI.e. P (v)1j＝1|h0) According to P (v)1j＝1|h0) Sampling to obtain v1j。
Calculating the mapping operation between the hidden layer and the hidden layer for the used hidden layer node iI.e. P (h)1i＝1|v1) According to P (h)1i＝1|v1) And updating the bias parameters of the connection weight. The update formula is as follows:
W＝W-ε(h0v0'-Q(h1＝1|v1)v1')
b＝b-ε(h0-Q(h1＝1|v1))
c＝c-ε(v0-v1)
(4) detecting faults of turbine blades of a combustion engine: a fault detection system for a turbine blade of a combustion engine for an algorithm experiment is constructed, a series of targeted experiments are carried out by taking the system as a platform, and the system is configured as follows:
hardware: the processor Intel (R) Pentium (R) Dual CPU 1.60 GHz; 2GB of memory; a display card 256M; and a hard disk 80G.
Software: windows XP operating system; matlab2012a development environment.
Simulating 6 types of fault data, 10 groups of samples of each type and 60 groups of data in total, carrying out EEMD decomposition on a simulated turbine blade fault signal, then solving the energy of IMF of the simulated turbine blade fault signal, selecting the first 8 components because fault information is mainly concentrated in the first components, wherein the capacity of residual terms is almost zero, and then solving a feature vector. The data were divided into two parts, 48 groups were randomly taken as training samples, and the remaining 12 groups were taken as test samples. And training the deep learning network by using the training samples. And classifying the samples to be tested by using the trained deep learning network, and outputting a classification result, wherein the accuracy is 91.6%.
Simulating 6 types of fault data, 10 groups of samples of each type and 60 groups of data in total, carrying out EEMD decomposition on a simulated turbine blade fault signal, then solving the energy of IMF of the simulated turbine blade fault signal, selecting the first 8 components because fault information is mainly concentrated in the first components, wherein the capacity of residual terms is almost zero, and then solving a feature vector. The data were divided into two parts, 48 groups were randomly taken as training samples, and the remaining 12 groups were taken as test samples. And training the deep learning network by using the training samples. Classifying the samples to be tested by using the trained deep learning network, outputting the classification result, and performing test comparison with a BP neural network and a Support Vector Machine (SVM), wherein the result is shown in table 1.
TABLE 1 comparison of diagnostic results
Claims (1)
1. A method for detecting faults of turbine blades of a combustion engine based on deep learning is characterized by comprising the following steps:
(1) the temperature data preprocessing of the turbine blade comprises the following steps:
(1.1) periodic Signal extraction
The turbine engine analyzed consisted of 86 blades, the data for radiation thermometry was obtained after a number of cycles of rotation of all the blades,
according to the discriminant formula of the periodic property: f (x) ═ f (x + T)
Wherein T is the period of the function, and whether the temperature data is period data is judged by the above formula; the minimum value and the maximum value are taken as the discrimination standard to carry out the period discrimination,
taking a certain maximum value point as a starting point, counting all the maximum value points in a certain section of interval, calling the points as test points, obtaining two adjacent test points with the same temperature, then respectively sampling backwards and comparing the temperatures of the sampling points, and if the two adjacent test points with the same temperature are the same, determining that the period is one;
(1.2) variable Condition temperature data distribution
The working conditions are divided into the following three working conditions: 0.6 operating mode, 0.8 operating mode and 1.0 operating mode, the turbine blade rotational speed under three kinds of operating modes respectively is: 8635/rpm, 8920/rpm, 9138/rpm;
(1.3) Single blade temperature data distribution
Establishing a segmentation algorithm for single blade temperature data on extreme value searching, counting maximum value points in the data, comparing the temperature data of each extreme value point, selecting a point with the maximum temperature data, namely the maximum value point, and marking as a0, searching backwards for a period, namely 40 sampling points, by taking a0 as a starting point, and recording the maximum value points in the period; judging the maximum value points, when the maximum value points cannot be larger than 10% of the adjacent maximum value points, if the maximum value points are larger than 10% of the adjacent maximum value points, changing the maximum value points into a starting point a0, circulating the process for 86 times, obtaining the period of 86 blades, carrying out sequence numbering on the blades from 01 to 86, and extracting the characteristics of the blades for state classification detection;
(2) extracting turbine blade temperature feature vectors
Extracting the temperature characteristic vector of the blade by adopting a method based on ensemble empirical mode decomposition; changing the number of extreme points and the distribution interval of the extreme points of the signal or data by adding different Gaussian noises with given amplitudes into the signal every time, and then carrying out overall average cancellation on IMF components obtained by multiple decomposition to cancel the noises added into the signal;
(2.1) initializing a noise amplitude value added to the signal;
(2.2) decomposing the ith added noise signal;
(2.2.1) adding a Gaussian signal n with a certain amplitude to the signal x (t)i(t)，xi(t)＝ni(t) + x (t), where the left side of the equation represents the signal formed after the i-th addition of the noise signal; the first term on the right of the equation represents the gaussian noise signal; x (t) represents the original signal;
(2.2.2) for xi(t) EMD decomposition to obtain a set of IMF components ciWherein c isiRepresenting IMF components obtained by the ith decomposition; i represents the number of decompositions; 1,2,3, ·, I;
(2.2.3) if I < I, returning to step (2.2.1) and so that I ═ I +1, repeating step (2.2.1) and step (2.2.2) until I ═ I;
(2.3) calculating the overall average value by the I decomposition, denoted by the symbol y;
(2.4) saving the total average value of all IMF components as a final IMF component;
(3) fault diagnosis based on deep learning network
Selecting a restricted Boltzmann machine model as a depth network design model, adjusting the restricted Boltzmann machine model through the progressive between layers, forming a transmission layer of the whole depth model by using 1 hidden layer and 1 visual output layer, and initializing the weight of the whole network;
the restricted boltzmann model is represented as RBM (W, b, c, v0), where W is a weight connection matrix between a first hidden layer and a second hidden layer, b is a bias of the hidden layers, c is a bias of the input layers, and v0 represents a sample set used in training;
(3.1) for all nodes i of the hidden layer, calculating mapping operation between the hidden layersI.e. P (h)0i＝1|v0) According to P (h)0i＝1|v0) Sampling to obtain h0iSigm () is a mapping function;
(3.2) for all nodes j of the visual layers, calculating the mapping operation between the visual layersI.e. P (v)1j＝1|h0) According to P (v)1j＝1|h0) Sampling to obtain v1j；
(3.3) for the used hidden layer node i, calculating the mapping operation between the hidden layer and the hidden layerI.e. P (h)1i＝1|v1) According to P (h)1i＝1|v1) Updating the bias parameters of the connection weight; the update formula is as follows:
W＝W-ε(h0v′0-P(h1＝1|v1)v′1)
b＝b-ε(h0-P(h1＝1|v1))
c＝c-ε(v0-v1)；
(4) detecting faults of turbine blades of a combustion engine: constructing a gas turbine blade fault detection system for an algorithm experiment, simulating 6 types of fault data, 10 groups of samples of each type, and 60 groups of data, carrying out EEMD decomposition on a simulated turbine blade fault signal, then solving the energy of IMF of the simulated turbine blade fault signal, selecting the first 8 components, enabling the capacity of a residual item to be zero, and then solving a feature vector; dividing the data into two parts, randomly taking 48 groups as training samples, and taking the other 12 groups as test samples; training the deep learning network by using the training samples; and classifying the samples to be tested by using the trained deep learning network, and outputting a classification result.
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